921 citations · 9.3k across the 292 of their papers we have counts for
6 papers · 2 filters
Strategies and Principles of Distributed Machine Learning on Big Data
Eric P. Xing, Qirong Ho, Pengtao Xie +1
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to dig…
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
Abhimanu Kumar, Pengtao Xie, Junming Yin +1
We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on…
Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums
Abhimanu Kumar, Shriphani Palakodety, Chong Wang +3
Online discussion forums are complex webs of overlapping subcommunities (macrolevel structure, across threads) in which users enact different roles depending on which subcommunity…
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Hannes Nickisch +4
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance st…
Embarrassingly Parallel Variational Inference in Nonconjugate Models
Willie Neiswanger, Chong Wang, Eric Xing
We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, w…
Bayesian Nonparametric Kernel-Learning
Junier Oliva, Avinava Dubey, Andrew G. Wilson +3
Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approxima…